ArticleThe plant genome2021
Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs.
Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Genetic analysis of predicted vegetative biomass and biomass-related traits from digital phenotyping of strawberry.The plant genome · 2025Article
- Improved genomic prediction performance with ensembles of diverse models.G3 (Bethesda, Md.) · 2025Article
- Combining genome-wide association and genomic prediction to unravel the genetic architecture of carotenoid accumulation in carrot.The plant genome · 2025Article
- Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage.BMC plant biology · 2025Article
- Assessment of the potential for genomic selection to improve resistance to fusarium stalk rot in maize.Frontiers in plant science · 2025Article
- Article
- Quantifying Genetic Parameters for Blackleg Resistance in Rapeseed: A Comparative Study.Plants (Basel, Switzerland) · 2024Article
- Genome-wide association study for yield and quality of granulated cassava processed product.The plant genome · 2024Article
- Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).The plant genome · 2024Article
- Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- PNNGS, a multi-convolutional parallel neural network for genomic selection.Frontiers in plant science · 2024Article
- Low-density SNP markers with high prediction accuracy of genomic selection for bacterial wilt resistance in tomato.Frontiers in plant science · 2024Article
- Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.The plant genome · 2023Article
- Haplotype-tagged SNPs improve genomic prediction accuracy for Fusarium head blight resistance and yield-related traits in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genome-wide marker effect heterogeneity is associated with a large effect dormancy locus in winter malting barley.The plant genome · 2022Article
- How Could the Use of Crop Wild Relatives in Breeding Increase the Adaptation of Crops to Marginal Environments?Frontiers in plant science · 2022Review
- Genome-wide association study, haplotype analysis, and genomic prediction reveal the genetic basis of yield-related traits in soybean (Frontiers in genetics · 2022Article
- Classification and Regression Models for Genomic Selection of Skewed Phenotypes: A Case for Disease Resistance in Winter Wheat (Frontiers in genetics · 2022Article
Corrections and comments
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Traits with a complex unknown genetic architecture are common in breeding programs. However, they pose a challenge for selection due to a combination of complex environmental and pleiotropic effects that impede the ability to create mapping populations to characterize the trait's genetic basis. One such trait, seedling emergence of wheat (Triticum aestivum L.) from deep planting, presents a unique opportunity to explore the best method to use and implement genetic selection (GS) models to predict a complex trait. Seventeen GS models were compared using two training populations, consisting of 473 genotypes from a diverse association mapping panel phenotyped from 2015 to 2019 and the other training population consisting of 643 breeding lines phenotyped in 2015 and 2020 in Lind, WA, with 40,368 markers. There were only a few significant differences between GS models, with support vector machines reaching the highest accuracy of 0.56 in a single breeding line trial using cross-validations. However, the consistent moderate accuracy of the parametric models indicates little advantage of using nonparametric models within individual years, but the nonparametric models show a slight increase in accuracy when combing years for complex traits. There was an increase in accuracy using cross-validations from 0.40 to 0.41 using diversity panels lines to breeding lines. Overall, our study showed that breeders can accurately predict and implement GS for a complex trait by using nonparametric machine learning models within their own breeding programs with increased accuracy as they combine training populations over the years.
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